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          五阿哥
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                <div class="bbp-reply-header" id="post-264767">
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                  <span class="bbp-reply-post-date">
                   2009年3月5日 上午3:19
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/14211/">
                    请求指点
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                  <p>
                   WEIZHI * DAXIAO1 Crosstabulation
                   <br/>
                   DAXIAO1
                   <br/>
                   1    2    Total
                   <br/>
                   WEIZHI    1    Count    64    23    87
                   <br/>
                   % within WEIZHI    73.6%    26.4%    100.0%
                   <br/>
                   2    Count    11    2    13
                   <br/>
                   % within WEIZHI    84.6%    15.4%    100.0%
                   <br/>
                   3    Count    26    12    38
                   <br/>
                   % within WEIZHI    68.4%    31.6%    100.0%
                   <br/>
                   4    Count    55    29    84
                   <br/>
                   % within WEIZHI    65.5%    34.5%    100.0%
                   <br/>
                   5    Count    11    18    29
                   <br/>
                   % within WEIZHI    37.9%    62.1%    100.0%
                   <br/>
                   6    Count    8    13    21
                   <br/>
                   % within WEIZHI    38.1%    61.9%    100.0%
                   <br/>
                   Total        Count    175    97    272
                   <br/>
                   % within WEIZHI    64.3%    35.7%    100.0%
                   <br/>
                   如果要具体写的话，该如何说明，请高手指点，
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                   2009年3月5日 上午3:16
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/14211/">
                    请求指点
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                   2 楼
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                  <p>
                   变量    分组    &lt;7mm（N）    ≥7mm（N）
                   <br/>
                   p值
                   <br/>
                   瘤的部位分部    Acoma    64    23    20.99    0.0008*
                   <br/>
                   ACA    11    2
                   <br/>
                   MCA    26    12
                   <br/>
                   Pcoma    55    29
                   <br/>
                   ICA    11    18
                   <br/>
                   其它    8    13
                  </p>
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                <div class="bbp-reply-header" id="post-261974">
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                   2009年1月7日 下午2:59
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13396/">
                    关于一组资料的设计，请求高手指点啊
                   </a>
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                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/13396/page/3/#post-261974">
                   43 楼
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                  <p>
                   算了吧，就按照那个统计学的研究生帮我做的结果去分析吧，都怪自己对统计学不精通，再次感谢hexm26对我的热心帮助，我想和你交个朋友，我的QQ309961080，再次感谢你对我的支持！
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                <div class="bbp-reply-header" id="post-261852">
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                   2009年1月6日 上午5:20
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                   回复：
                   <a class="bbp-topic-permalink" href="http://cos.name/cn/topic/13396/">
                    关于一组资料的设计，请求高手指点啊
                   </a>
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                  <a class="bbp-reply-permalink" href="http://cos.name/cn/topic/13396/page/3/#post-261852">
                   38 楼
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                  <p>
                   没有，我没有冠心病的指标，只是说了个例子，那我刚才自己做了个logistic回归，是把血压和位置这两个单因素有统计学意义的变量做logistic回归,不是Multiple Logistic Regression是做的Binary Regression,几点说明(血压中1代表高血压，0代表没有高血压；位置中有1、2、3、4、5、6、个位置）结果如下：    Case Processing Summary
                   <br/>
                   Unweighted Casesa        N    Percent
                   <br/>
                   Selected Cases    Included in Analysis    272    100.0
                   <br/>
                   Missing Cases    0    .0
                   <br/>
                   Total    272    100.0
                   <br/>
                   Unselected Cases        0    .0
                   <br/>
                   Total        272    100.0
                   <br/>
                   a. If weight is in effect, see classification table for the total number of cases.
                  </p>
                  <p>
                   Dependent Variable Encoding
                   <br/>
                   Original Value    Internal Value
                   <br/>
                   1    0
                   <br/>
                   2    1
                  </p>
                  <p>
                   Iteration History(a)(,)(b)(,)(c)
                   <br/>
                   Coefficients
                   <br/>
                   Iteration        -2 Log likelihood    Constant
                   <br/>
                   Step 0    1    377.013    .029
                   <br/>
                   2    377.013    .029
                   <br/>
                   a. Constant is included in the model.
                   <br/>
                   b. Initial -2 Log Likelihood: 377.013
                   <br/>
                   c. Estimation terminated at iteration number 2 because parameter estimates changed by less than .001.
                   <br/>
                   Classification Table(a)(,)(b)
                   <br/>
                   Predicted
                   <br/>
                   DAXIAO2
                   <br/>
                   Observed        1    2    Percentage Correct
                   <br/>
                   Step 0    DAXIAO2    1    0    134    .0
                   <br/>
                   2    0    138    100.0
                   <br/>
                   Overall Percentage                50.7
                   <br/>
                   a. Constant is included in the model.
                   <br/>
                   b. The cut value is .500
                  </p>
                  <p>
                   Variables in the Equation
                   <br/>
                   B    S.E.    Wald    df    Sig.    Exp(B)
                   <br/>
                   Step 0    Constant    .029    .121    .059    1    .808    1.030
                  </p>
                  <p>
                   Variables not in the Equation
                   <br/>
                   Score    df    Sig.
                   <br/>
                   Step 0    Variables    XUEYA    9.807    1    .002
                   <br/>
                   WEIZHI    6.128    1    .013
                   <br/>
                   Overall Statistics        15.678    2    .000
                  </p>
                  <p>
                   Iteration History(a)(,)(b)(,)(c)(,)(d)
                   <br/>
                   Coefficients
                   <br/>
                   Iteration        -2 Log likelihood    Constant    XUEYA    WEIZHI
                   <br/>
                   Step 1    1    360.977    .052    -.814    .177
                   <br/>
                   2    360.939    .057    -.854    .187
                   <br/>
                   3    360.939    .057    -.854    .187
                   <br/>
                   a. Method: Enter
                   <br/>
                   b. Constant is included in the model.
                   <br/>
                   c. Initial -2 Log Likelihood: 377.013
                   <br/>
                   d. Estimation terminated at iteration number 3 because parameter estimates changed by less than .001.
                  </p>
                  <p>
                   Omnibus Tests of Model Coefficients
                   <br/>
                   Chi-square    df    Sig.
                   <br/>
                   Step 1    Step    16.075    2    .000
                   <br/>
                   Block    16.075    2    .000
                   <br/>
                   Model    16.075    2    .000
                  </p>
                  <p>
                   Model Summary
                   <br/>
                   Step    -2 Log likelihood    Cox &amp; Snell R Square    Nagelkerke R Square
                   <br/>
                   1    360.939a    .057    .077
                   <br/>
                   a. Estimation terminated at iteration number 3 because parameter estimates changed by less than .001.
                   <br/>
                   Hosmer and Lemeshow Test
                   <br/>
                   Step    Chi-square    df    Sig.
                   <br/>
                   1    6.142    6    .407
                  </p>
                  <p>
                   Contingency Table for Hosmer and Lemeshow Test
                   <br/>
                   DAXIAO2 = 1.00        DAXIAO2 = 2.00
                   <br/>
                   Observed    Expected    Observed    Expected    Total
                   <br/>
                   Step 1    1    39    38.238    20    20.762    59
                   <br/>
                   2    22    22.255    17    16.745    39
                   <br/>
                   3    34    29.730    24    28.270    58
                   <br/>
                   4    5    9.319    15    10.681    20
                   <br/>
                   5    10    12.304    18    15.696    28
                   <br/>
                   6    10    9.795    15    15.205    25
                   <br/>
                   7    9    8.039    17    17.961    26
                   <br/>
                   8    5    4.321    12    12.679    17
                  </p>
                  <p>
                   Classification Table(a)
                   <br/>
                   Predicted
                   <br/>
                   DAXIAO2
                   <br/>
                   Observed        1    2    Percentage Correct
                   <br/>
                   Step 1    DAXIAO2    1    95    39    70.9
                   <br/>
                   2    61    77    55.8
                   <br/>
                   Overall Percentage                63.2
                   <br/>
                   a. The cut value is .500
                  </p>
                  <p>
                   Variables in the Equation
                   <br/>
                   95.0% C.I.for EXP(B)
                   <br/>
                   B    S.E.    Wald    df    Sig.    Exp(B)    Lower    Upper
                   <br/>
                   Step 1a    XUEYA    -.854    .276    9.562    1    .002    .426    .248    .731
                   <br/>
                   WEIZHI    .187    .076    6.018    1    .014    1.205    1.038    1.399
                   <br/>
                   Constant    .057    .323    .031    1    .861    1.058
                   <br/>
                   a. Variable(s) entered on step 1: XUEYA, WEIZHI.
                  </p>
                  <p>
                   Correlation Matrix
                   <br/>
                   Constant    XUEYA       WEIZHI
                   <br/>
                   Step 1    Constant    1.000    -.577    -.693
                   <br/>
                   XUEYA       -.577    1.000    -.044
                   <br/>
                   WEIZHI      -.693    -.044    1.000
                  </p>
                  <p>
                   Step number: 1
                  </p>
                  <p>
                   Observed Groups and Predicted Probabilities
                  </p>
                  <p>
                   80 ┼                                                                                                    ┼
                   <br/>
                   │                                                                                                    │
                   <br/>
                   │                                                                                                    │
                   <br/>
                   F        │                                                                                                    │
                   <br/>
                   R     60 ┼                                   2            2                                                   ┼
                   <br/>
                   E        │                                   2            2                                                   │
                   <br/>
                   Q        │                                   2            2                                                   │
                   <br/>
                   U        │                                   2            2                                                   │
                   <br/>
                   E     40 ┼                                   1            2                                                   ┼
                   <br/>
                   N        │                                   1            1                                                   │
                   <br/>
                   C        │                                   1        2   1       2                                           │
                   <br/>
                   Y        │                                   1        2   1       2            2                              │
                   <br/>
                   20 ┼                                   1        2   1    2  2            2                              ┼
                   <br/>
                   │                                   1        1   1    2  2 2          2                              │
                   <br/>
                   │                                   1   2    1   1    2  1 2     2    1  2   2                       │
                   <br/>
                   │                                   1   1    1   1    1  1 1 2   1    1  1   2                       │
                   <br/>
                   Predicted ─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼──────────
                   <br/>
                   Prob:   0       .1        .2        .3        .4        .5        .6        .7        .8        .9         1
                   <br/>
                   Group:  1111111111111111111111111111111111111111111111111122222222222222222222222222222222222222222222222222
                  </p>
                  <p>
                   Predicted Probability is of Membership for 2.00
                   <br/>
                   The Cut Value is .50
                   <br/>
                   Symbols: 1 – 1.00
                   <br/>
                   2 – 2.00
                   <br/>
                   Each Symbol Represents 5 Cases.
                  </p>
                  <p>
                   怎么这个logistic回归里血压好像有意义啊？
                  </p>
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                <div class="bbp-reply-header" id="post-261841">
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                   2009年1月6日 上午3:49
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                  <p>
                   他说是用的非条件logistic回归做的，麻烦了他N次，我不好意思让他在再做了，我想自己做做看望您指导一二，还有我也问过几位统计学博导，他们说血压可能受了其它因素的影响，比如血压和冠心病的关系比较密切，后者可能会导致logistic回归里不出现血压这种情况，您看怎么办？
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                <div class="bbp-reply-header" id="post-261785">
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                   2009年1月5日 上午6:39
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                   回复：
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                  <p>
                   他说血压没有进入模型，没有P值
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                <div class="bbp-reply-header" id="post-261633">
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                   2009年1月3日 上午3:03
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                  <p>
                   logisyic回归是我请搞统计的学生帮我做的，等我明天回去和他联系后再告诉你，记得他说过血压没有进入模型什么的
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                <div class="bbp-reply-header" id="post-261513">
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                   2009年1月1日 下午2:38
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                  <p>
                   (χ2=20.99, p =0.0008)这是位置单因素卡方检验的结果
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                   2009年1月1日 下午2:37
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                   回复：
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                  <p>
                   血压的卡方检验（χ2=5.14, p =0.023），lofistic回归的P值24楼表里有的，谢谢您！祝你新年快乐！
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                <div class="bbp-reply-header" id="post-261305">
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                   2008年12月29日 上午4:44
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                  <p>
                   非常感谢，终于有人明白我的意思了，就是说嘛，曾经有人叫我这篇文章投国内顶级核心期刊，还有一个问题就是现在单因素分析有血压和位置两个因素在&lt;7MM和&gt;7MM的瘤之间有统计学差异，Logistic回归分析只有位置一个有统计学差异，那么我的结论是否应该以Logistic回归为主，还有最理想的结论(为了不让人误解我是研究动脉瘤破裂的原因)您看如何下好，谢谢，我原本是这样下的：相对于&gt;7MM的颅内破裂动脉瘤而言，位置A和位置B可能更容易导致颅内&lt;7MM的小动脉瘤破裂。再次感谢！
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                <div class="bbp-reply-header" id="post-261001">
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                   2008年12月22日 下午12:20
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                  <p>
                   我白天上班，只有晚上回来后才能看到你的留言
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                <div class="bbp-reply-header" id="post-261000">
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                   2008年12月22日 下午12:19
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                  <p>
                   把位置分为两组在医学上是说不通的，这是6个不同的位置，我要看这6个位置中，哪个对破裂小动脉瘤影响大，我叫的那个人，就是把所有因素都考虑进去，然后做 Logistic Regression，最后就得出下面的结论
                   <br/>
                   变量    参数值    标准误    OR(95%CI)
                   <br/>
                   p值
                   <br/>
                   前交通动脉    1.5900    0.5727    4.904(1.596-15.067)    7.7077    0.0055
                   <br/>
                   大脑前动脉    1.5210    0.4708    4.577(1.819-11.517)    10.4375    0.0012
                   <br/>
                   连单因素分析出的有统计学意义的血压都被排除在外
                   <br/>
                   所以我文章的最后的结论就是位于前交通动脉和大脑前动脉的动脉瘤建议尽早治疗。第二篇文章我不知道怎么发给你
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                   2008年12月21日 上午6:09
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                   回复：
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                    关于一组资料的设计，请求高手指点啊
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                   方便的话，我可以把这两篇文章发到你的邮箱里
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                   2008年12月21日 上午6:01
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                    关于一组资料的设计，请求高手指点啊
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                   1）Size of cerebral aneurysms and related factors in patients with subarachnoid hemorrhage
                   <br/>
                   2）Factors Associated with Aneurysm Size in Patients with Subarachnoid Hemorrhage: Effect of Smoking and Aneurysm Location
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                   2008年12月21日 上午5:18
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                   没有破的动脉瘤实在太少，收集起来非常困难，我收集了两年的动脉瘤，没有破的不超过10个，时间和精力都不允许我这样，照您看课题是否做到单因素分析出上述两个变量（血压和位置）有统计学意义就够了，假如这样的话我的结论该如何下？另外我搜到几篇国外文献，他们研究的也全部是破裂动脉瘤，有一篇是先用单因素分析，然后用logist回归，还有一篇直接用logist回归进行分析。所以我的文章才想到也用logist回归分析的，您认为不可以的话我就不用，省得画蛇添足。
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